Social media data analysis system and method

ABSTRACT

A system analyzes data to determine an activity around a product. The system includes a user interface configured to enable one or more data analysts to provide input data and an acquisition module coupled to user interface and configured to retrieve social media data in response to the input data. The social media data is received from one more social media platforms. The system further includes processing circuitry coupled to the acquisition module and includes an analysis module configured to analyze the social media data to generate processed data and classify the processed data based on a plurality of criteria and a visualization module coupled to the analysis module and configured to generate a plurality of visual representations of classified data.

BACKGROUND

The present invention is related to data analysis systems andtechniques. More particularly the present invention is related toanalyzing data received from various social media platforms to assist informing a strategy for various products and businesses.

In recent times, there is increasing awareness of the role of socialmedia in influencing customers and business stakeholders. The presenceof social media platforms has matured to becoming a key component ofmarketing strategy. The increasing volume of information existing onsocial media platforms such as Facebook, Twitter, etc. available todayreinforces the need to develop the correct strategy for customers.Factors like social buzz, trends, consumer feedback and opinion, marketsentiments need to be integrated into any business decision.

Most business organizations are now keen to accurately identify itsonline customer base, effectively communicate with their existingcustomers, understand a tone of market response to their business,manage vast expanse of information and, discover the impact of socialmedia on the overall business.

However, some of the important challenges with existing solutions arethe difficulty in accurately identifying impact sources for businessoutcomes and objectives and aligning social media intelligence withbusiness goals. There is also an inability to present statistics in acomprehensible way to users. Moreover, managing vast expanses of data toderive meaningful insights to support business processes and use casesare also hard to achieve.

Therefore, there is a need for a system and a method that can processinformation retrieved from various social media platforms to determine aresponse to a business or a product. Also, there is a need to representand classify such information in an accurate and effective way to users,which will enable them to make the right business decisions.

SUMMARY

Briefly, according to one embodiment of the invention, a system foranalyzing data to determine an activity around a product is provided.The system comprises a user interface configured to enable one or moredata analysts to provide input data and an acquisition module configuredto retrieve social media data in response to the input data. The socialmedia data is received from one or more social media platforms. Thesystem further comprises processing circuitry coupled to the acquisitionmodule and comprises an analysis module configured to analyze the socialmedia data to generate processed data and classify the processed databased on a plurality of criteria and a visualization module coupled tothe analysis module and configured to generate a plurality of visualrepresentations of classified data.

In another embodiment, a method for analyzing data received from aplurality of social media platforms is provided. The method comprisesretrieving social media data from the plurality of social mediaplatforms based on input data provided by one or more data analysts,processing the social media data by applying one or more text analysismodels to generate text data. The method further includes classifyingthe text data based on one or more criteria and generating one or morevisual representations of the processed data based on the one or morecriteria.

In another embodiment, a computer program containing computer executableinstructions for analyzing data, comprising at least one computerreadable medium and code stored on the at least one computer readablemedium encoding routines is provided. The computer program includesroutines for receiving social media data from a plurality of sourcesbased on an input data, processing the social media data to generatetext data by applying one or more text analysis models. Further, thetext data is classified based on one or more criteria; wherein the oneor more criteria comprise a positive sentiment, a neutral sentiment anda negative sentiment. One or more visual representations of the textdata are generated based on the one or more criteria. In addition, aplurality of key influencers contributing to a behavior of the socialmedia data is determined and a plurality of alerts is generated based onthe input data.

DRAWINGS

These and other features, aspects, and advantages of the presentinvention will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a block diagram of an embodiment of a data analysis systemimplemented according to aspects of the present technique;

FIG. 2 is a block diagram of an embodiment of processing circuitryimplemented according to aspects of the present technique;

FIG. 3 is a flow chart illustrating one method by which social mediadata is analyzed;

FIG. 4 is a block diagram of a general purpose computer implementedaccording to aspects of the present technique; and

FIG. 5 to FIG. 13 illustrates example screen shots of a graphical userinterface implemented according to aspects of the present technique.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented herein. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, separated, anddesigned in a wide variety of different configurations, all of which areexplicitly contemplated herein.

Example embodiments are generally directed to data analysis systems foranalyzing data received from several sources. The following descriptionis with reference to analyzing data received from various social mediaplatforms, however it should be understood that the techniques describedherein may be applied in for data received from other sources such asthe World Wide Web, various databases, and the like.

FIG. 1 is a block diagram of an embodiment of a data analysis system incommunication with various social media platforms. The data analysissystem 10 is configured to analyze social media data retrieved fromvarious social media sources to determine an activity around a productor a business. Each block of the data analysis system 10 is described infurther detail below.

The data analysis system 10 is configured to connect with various socialmedia platforms 24, 26 and 28 through a communication networks such asthe Internet 22. Examples of social media platforms include Facebook,Twitter and the like. For the purposes of this specification and claims,the term “social media platform” may relate to any type of computerizedmechanism through which persons may connect or communicate with eachother. Some social media platforms may be applications that facilitateend-to-end communications between users in a formal manner. Other socialnetworks may be less formal, and may consist of a user's email contactlist, phone list, mailing list, or other database from which a user mayinitiate or receive communication. Also, it may be noted that, the term“user” may refer to both natural people and other entities that operateas a “user”. Examples include corporations, organizations, enterprises,teams, or other group of peoples.

The data analysis system 10 includes a user interface 14, which isconfigured to enable one or more data analysts to provide input data. Asused herein, a data analyst refers to an entity that performs dataanalysis on social media data. The user interface may include varioustypes of devices such as keyboards, etc. Input data may includekeywords, trade names of a particular product, names of organizations,etc. In one embodiment, the input data is provided to the data analysissystem via a graphical user interface that is displayed on display unit12.

Acquisition module 16 is configured to retrieve social media data inresponse to the input data that was provided by the users. As usedherein, “social media data” refers to data present in the various socialmedia platforms such as text data, user profiles, geographic locations,and the like.

Processing circuitry 18 coupled to the acquisition module and isconfigured to process the social media data to generate processed data.The processed data can be used to determine various activities aroundthe input data that was provided by the data analyst. For example, ifthe input data was a specific product, the social media data isprocessed to determine a buzz around the product, positive and negativesentiments about the product, the different geographical locations thatare impacting the product, people that are influencing the product'ssales and the like. In one embodiment, these various activities arepresented to the data analyst user in the form of visual representationssuch as graphs, charts, etc.

Memory circuitry 20 is coupled to processing circuitry and configured tostore the social media data, processed data and the one or more visualrepresentations that are generated by the processing circuitry 18. Themanner is which processing circuitry analyses the social media data isdescribed in further detail below.

FIG. 2 is a block diagram of an embodiment of processing circuitryimplemented according to aspects of the present technique. Processingcircuitry 18 comprises analysis module 22, notification module 24 andvisualization module 26. Each component is described in further detailbelow.

Analysis module 22 is configured to receive the social media data fromvarious social media platforms. The social media data corresponds toinput data provided by a data analyst. In one embodiment, the analysismodule is configured to pre-process the social media data to filternon-relevant data. Several well known models can be applied such as spamfiltering algorithms to remove content not related to the business,“part of speech tagging” to extract language components like nouns,verbs, adjectives, etc. In addition, stemming operations to normalize oftext data and other custom filters like removal of stop words i.e.,generic words which do not make any sense during analysis like a, an,the etc., phone numbers, email ids, etc may also be applied.

The analysis module 22 is configured to analyze the social media dataand generate processed data. In one embodiment, text analysis models areapplied on the social media data. Examples of text analysis modelsinclude text frequency analysis, sentiment analysis and topic modeling.Further, the analysis module is configured to access historical datapertinent to the input data while processing the social media data. Suchhistorical data is stored in memory circuitry 20.

Further, the analysis module 22 is configured to classify the processeddata based on a plurality of criteria. Examples of such criteria includesentiments, geographic locations, authors, and the like. In oneembodiment, the criteria are selected by the user. The criteria may bepresented to the user as a drop down menu, check box menu, etc. In oneembodiment, the data analyst is provided an option to select more thanone criterion for classification.

Notification module 24 is coupled to the analysis module 24 and isconfigured to generate a plurality of alerts to one or more users basedon the input data. These alerts are generated based on the input dataand the processed data and can be sent regularly over a selected periodof time.

Visualization module 26 is coupled to the analysis module 22 and isconfigured to generate a plurality of visual representations ofprocessed data classified based on the plurality of criteria. The visualrepresentations aid in presenting a complete picture of the social mediadata that was retrieved. These representations allow a data analyst tomake informed decisions on a product or a business. The manner in whichsocial media data is processed and visual representations are generatedare described in further detail below.

FIG. 3 is a flow chart illustrating one method by which social mediadata is processed according to aspects of the present technique. Asdescribed above, social media data refers to data retrieved from socialmedia platforms that exist today. In one embodiment, the data isretrieved in real-time. The process 30 for analyzing social media datais described in further detail below.

At step 32, social media data is retrieved from one or more social mediaplatforms. The social media data is retrieved in response to input dataprovided by a data analyst. In general, input data may include keywordsfor a certain product, the product name, a name of a business or anorganization, etc. In one embodiment, social media data includes textstrings.

When the social media data is retrieved, data analysis operations areperformed on the social media data. In the illustrated embodiment, thedata analysis operations include text analysis as described in detailbelow with reference to step 34 and 36. However, it must be understoodto one skilled in the art, that other data analysis operations may alsobe performed on the social media data.

At step 34, a text analysis model is applied on the social media data togenerate a cluster of text data. Text analysis models typicallystructure the social media data, determine specific patterns within thestructured data, and evaluate and interpret the data. In one embodiment,the social media data is first pre-processed using standardpre-processing algorithms to filter non-relevant data before applyingthe text analysis model.

Examples of text analysis models include frequency analysis, sentimentanalysis and topic modeling. In one embodiment, text data frequencyanalysis is performed on the text data to determine a number of timescertain words of interest repeat within the extracted text data.

In one embodiment, sentiment analysis models are applied on the clusterof text data to generate a sentiment analysis data spectrum. Sentimentanalysis models are used to classify the text data according to one ormore sentiments that is expressed. In one embodiment, the text data isclassified based on a positive sentiment, a neutral sentiment and anegative sentiment.

In another embodiment, topic modeling is performed on the text data.Topic modeling schemes enable the identification of several themes thatare present in the text data. Further, the topic modeling schemesdetermine a relative importance of each word within a topic.

At step 38, a set of key influencers is determined based on the textdata and the sentiment analysis data spectrum. Typically, an onlineuser's influence is determined by analyzing the online profile of theuser. Key influencers may be persons whose reputation and influence mayimpact the business or product. For example, an author of weblogs orother publications, or a person who comments or participates in onlinediscussions may be considered to have expertise in certain categories orcontexts. It is often advantageous to understand the manner in whichsuch influencers impact a product or a business.

At step 40, visual representations are created to illustrate to the dataanalyst the various results based on the text analysis and the sentimentanalysis performed on the social media data. In one embodiment, thevisual representations comprise trend and distribution charts. Suchcharts assist a user in making creating informed and accurate strategiesfor a product or a business.

At step 42, alerts and notifications are generated and provided to aplurality of users. In one embodiment, the alerts are configured toprogressively display more information in response to received inputdata. In particular, for example, an alert may initially provide a firstlevel of information or detail about a particular product. In responseto the data analysis steps 34 and 36 described above, a subsequent alertmay provide a second level of information or detail (e.g., moreinformation or detail than is provided in the first level) about theproduct. In one embodiment, the alerts and notifications are providedfor a particular period of time.

The technique described above can be performed by the data analysissystem described in FIG. 1 and FIG. 2. The technique described above maybe embodied as devices, systems, methods, and/or computer programproducts. Accordingly, some or all of the subject matter described abovemay be embodied in hardware and/or in software (including firmware,resident software, micro-code, state machines, gate arrays, etc.)Furthermore, the subject matter may take the form of a computer programproduct such as an analytical tool, on a computer-usable orcomputer-readable storage medium having computer-usable orcomputer-readable program code embodied in the medium for use by or inconnection with an instruction execution system. In the context of thisdescription, a computer-usable or computer-readable medium may be anymedium that can contain, store, communicate, propagate, or transport theprogram for use by or in connection with the instruction executionsystem, apparatus, or device.

The computer-usable or computer-readable medium may be, for example butnot limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, device, or propagationmedium. By way of example, and not limitation, computer readable mediamay comprise computer storage media and communication media.

When the subject matter is embodied in the general context ofcomputer-executable instructions, the embodiment may comprise programmodules, executed by one or more systems, computers, or other devices.Generally, program modules include routines, programs, objects,components, data structures, etc. that perform particular tasks orimplement particular abstract data types. Typically, the functionalityof the program modules may be combined or distributed as desired invarious embodiments.

FIG. 4 is a block diagram illustrating an embodiment of a computer 100that is configured to analyze social media data retrieved from varioussocial media platforms. The computer 100 is configured to executeinstructions for a data analysis tool that performs the steps describedin FIG. 3. In a very basic configuration 102, computer 100 typicallyincludes one or more processors 104 and a system memory 106. A memorybus 124 may be used for communicating between processor 104 and systemmemory 106.

Depending on the desired configuration, processor 104 may be of any typeincluding but not limited to a microprocessor (μP), a microcontroller(μC), a digital signal processor (DSP), or any combination thereof.Processor 104 may include one more levels of caching, such as a levelone cache 110 and a level two cache 112, a processor core 114, andregisters 116. An example processor core 114 may include an arithmeticlogic unit (ALU), a floating point unit (FPU), a digital signalprocessing core (DSP Core), or any combination thereof. An examplememory controller 118 may also be used with processor 104, or in someimplementations memory controller 118 may be an internal part ofprocessor 104.

Depending on the desired configuration, system memory 106 may be of anytype including but not limited to volatile memory (such as RAM),non-volatile memory (such as ROM, flash memory, etc.) or any combinationthereof. System memory 106 may include an operating system 120, one ormore applications 122, and program data 124. Application 122 include adata analysis tool 120 that is arranged to analyze social media datareceived from several social media platforms. Program data 126 mayinclude social media data. In some embodiments, application 122 may bearranged to operate with program data 126 on operating system 120 suchthat interaction between the dispensing devices and external entitiesare monitored. This described basic configuration 102 is illustrated inFIG. 4 by those components within the inner dashed line.

Computer 100 may have additional features or functionality, andadditional interfaces to facilitate communications between basicconfiguration 102 and any required devices and interfaces. For example,a bus/interface controller 130 may be used to facilitate communicationsbetween basic configuration 102 and one or more data storage devices 132via a storage interface bus 138. Data storage devices 132 may beremovable storage devices 134, non-removable storage devices 136, or acombination thereof. Examples of removable storage and non-removablestorage devices include magnetic disk devices such as flexible diskdrives and hard-disk drives (HDD), optical disk drives such as compactdisk (CD) drives or digital versatile disk (DVD) drives, solid statedrives (SSD), and tape drives to name a few. Example computer storagemedia may include volatile and nonvolatile, removable and non-removablemedia implemented in any method or technology for storage ofinformation, such as computer readable instructions, data structures,program modules, or other data.

System memory 106, removable storage devices 134 and non-removablestorage devices 136 are examples of computer storage media. Computerstorage media includes, but is not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage devices, or any other mediumwhich may be used to store the desired information and which may beaccessed by computer 100. Any such computer storage media may be part ofcomputer 100.

Computer 100 may also include an interface bus 138 for facilitatingcommunication from various interface devices (e.g., output devices 140,peripheral interfaces 148, and communication devices 160) to basicconfiguration 102 via bus/interface controller 130. Example outputdevices 142 include a graphics processing unit 144 and an audioprocessing unit 146, which may be configured to communicate to variousexternal devices such as a display or speakers via one or more A/V ports142. Example peripheral interfaces 148 include a serial interfacecontroller 150 or a parallel interface controller 152, which may beconfigured to communicate with external devices such as input devices(e.g., keyboard, mouse, pen, voice input device, touch input device,etc.) or other peripheral devices (e.g., printer, scanner, etc.) via oneor more I/O ports 148. An example communication device 160 includes anetwork controller 154, which may be arranged to facilitatecommunications with one or more other computer s 158 over a networkcommunication link via one or more communication ports 156.

The network communication link may be one example of a communicationmedia. Communication media may typically be embodied by computerreadable instructions, data structures, program modules, or other datain a modulated data signal, such as a carrier wave or other transportmechanism, and may include any information delivery media. A “modulateddata signal” may be a signal that has one or more of its characteristicsset or changed in such a manner as to encode information in the signal.By way of example, and not limitation, communication media may includewired media such as a wired network or direct-wired connection, andwireless media such as acoustic, radio frequency (RF), microwave,infrared (IR) and other wireless media. The term computer readable mediaas used herein may include both storage media and communication media.

Computer 100 may be implemented as a portion of a small-form factorportable (or mobile) electronic device such as a cell phone, a personaldata assistant (PDA), a personal media player device, a wirelessweb-watch device, a personal headset device, an application specificdevice, or a hybrid device that include any of the above functions.Computer 100 may also be implemented as a personal computer includingboth laptop computer and non-laptop computer configurations. Asdescribed above, the data analysis tool and system is configured toanalyze social media data retrieved from social media platforms. Thedata analysis tool and system may include a graphical user interface tofacilitate a user to provide input data. Some example user interfacescreens are described below with reference to FIG. 5 through FIG. 11.

FIG. 5 is a screen shot of a graphical user interface that enables adata analyst to provide input data to the data analysis system. The dataanalysis system enables the data analyst to provide a selection of keywords in a tab provided as shown in screen 44. The data analyst may alsoprovide information related to the keywords that are not relevant. Inaddition, the data analyst may select the various social media platformsof interest.

FIG. 6 is a screen shot of a visual representation of negative sentimentdata classified from a social media data. As can be clearly seen, thescreen shot 48 demonstrates the classification of the text data based onpositive, negative and neutral sentiments. The screen shot 48illustrates the negative words and a frequency of the negative words.The screen shot 46 of FIG. 7 illustrates the frequency of positivetweets, negative tweets and neutral tweets retrieved for various groupsin response the input data “Xbox”.

Similarly, the screen shot 52 illustrates all social media dataretrieved for the input data “xbox” as shown in FIG. 8. The bar chartillustrates the frequency of the input data at different instants oftime. FIG. 9 illustrates the various topics that have been derived forthe input data “Siri”. Topic modeling schemes enable the identificationof several themes that are present in the text data. Further, the topicmodeling schemes determine a relative importance of each word within atopic. For example in screen shot 52, the words, which are morerelevant, are indicated in larger boxes. In one embodiment, a colorscheme is also implemented to indicate the relevancy of each word.

FIG. 10 illustrates opinion trends for the input data “xbox”. Screenshot 54 illustrates the positive and negative opinion graph for theinput data as seen on Dec. 21, 2011. FIG. 11 illustrates key influencersfor the input data “xbox”. As can be seen in screen shot 56, the keyinfluencers are identified. In addition, the social profile of eachinfluencer is also made readily available. FIG. 12 illustrateshistorical data from Twitter for the input data “Big Data” and “Hadoop”.Screen shot 58 illustrates tweets retrieved from Twitter for the inputdata and corresponding metrics. FIG. 13 is a screen shot illustratingthe various geographic locations associated with the text data. Screenshot 60 illustrated the source of text data on a world map as shown.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present.

For example, as an aid to understanding, the following appended claimsmay contain usage of the introductory phrases “at least one” and “one ormore” to introduce claim recitations. However, the use of such phrasesshould not be construed to imply that the introduction of a claimrecitation by the indefinite articles “a” or “an” limits any particularclaim containing such introduced claim recitation to embodimentscontaining only one such recitation, even when the same claim includesthe introductory phrases “one or more” or “at least one” and indefinitearticles such as “a” or “an” (e.g., “a” and/or “an” should beinterpreted to mean “at least one” or “one or more”); the same holdstrue for the use of definite articles used to introduce claimrecitations. In addition, even if a specific number of an introducedclaim recitation is explicitly recited, those skilled in the art willrecognize that such recitation should be interpreted to mean at leastthe recited number (e.g., the bare recitation of “two recitations,”without other modifiers, means at least two recitations, or two or morerecitations).

While only certain features of several embodiments have been illustratedand described herein, many modifications and changes will occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all such modifications and changes as fallwithin the true spirit of the invention.

The invention claimed is:
 1. A system for analyzing data to determine anactivity around a product, the system comprising: a user interfaceconfigured to enable one or more data analysts to provide input dataregarding the product; and a computer processor configured to, retrievesocial media data in response to the input data, wherein the socialmedia data is received from one or more social media platforms, applytext analysis to the social media data, the text analysis being based onat least one of frequency analysis, sentiment analysis and topicmodeling, determine a set of users associated with the retrieved socialmedia data as key influencers, the key influencers indicating how socialstatus of the set of users associated with the social media datainfluence commercialization of the product, generate processed databased on the applied text analysis and the determined key influencers,generate a plurality of visual representations associated with theprocessed data to be presented to the one or more data analysts via theuser interface, and present, simultaneously with one or more of theplurality of visual representations, a social profile of each user fromamong the key influencers to the one or more data analysts via the userinterface, wherein the social profile of each user from among the keyinfluencers includes at least information about a device associated withthe user, a location associated with the user, a number of friends ofthe user, a number of other users considering the user as favorite, anda number of followers of the user.
 2. The system of claim 1, furthercomprising: a memory in communication with the computer processor andconfigured to store the social media data, the processed data and theplurality of visual representations.
 3. The system of claim 1, whereinthe computer processor is further configured to, pre-process the socialmedia data to filter non-relevant data, and apply the text analysis tothe pre-processed social media data.
 4. The system of claim 1, whereinthe computer processor is further configured to generate a plurality ofalerts to be sent to the one or more data analysts based on the inputdata, each of the plurality of alerts indicating a progress ingeneration of the processed data.
 5. The system of claim 1, wherein thecomputer processor is configured to, access historical data pertinent tothe input data, and utilize the historical data in generating theprocessed data.
 6. The system of claim 1, wherein the sentiment analysisincludes classifying the social media data based on a positivesentiment, a neutral sentiment and a negative sentiment.
 7. The systemof claim 1, wherein the plurality of visual representations includetrend and distribution charts.
 8. A method utilizing at least acomputing device for analyzing data received from a plurality of socialmedia platforms, the method comprising: retrieving social media data inresponse to the input data, wherein the social media data is receivedfrom one or more social media platforms; applying text analysis to thesocial media data, the text analysis being based on at least one offrequency analysis, sentiment analysis and topic modeling; determining aset of users associated with the retrieved social media data as keyinfluencers, the key influencers indicating how social status of the setof users associated with the social media data influencecommercialization of the product; generating processed data based on theapplied text analysis and the determined key influencers; generating aplurality of visual representations associated with the processed datato be presented to the one or more data analysts via the user interface,and presenting, simultaneously with one or more of the plurality ofvisual representations, a social profile of each user from among the keyinfluencers to the one or more data analysts via the user interface,wherein the social profile of each user from among the key influencersincludes at least information about a device associated with the user, alocation associated with the user, a number of friends of the user, anumber of other users considering the user as favorite, and a number offollowers of the user.
 9. The method of claim 8, further comprising:storing the social media data, text data and the plurality of visualrepresentations.
 10. The method of claim 8, further comprising:pre-processing the social media data to filter non-relevant data, uponretrieving the social media data, wherein the applying applies the textanalysis to the pre-processed social media data.
 11. The method of claim8, further comprising: mapping the social media data to a plurality ofgeographic locations, wherein the generating further generates theprocessed data based on the plurality of geographical locations.
 12. Themethod of claim 8, further comprising: generating a plurality of alertsbased on the input data, each of the plurality of alerts indicating aprogress in generation of the processed data.
 13. The method of claim 8,further comprising: accessing historical data pertinent to the inputdata, wherein the generating further generates the processed data basedon the historical data.
 14. The method of claim 8, wherein the sentimentanalysis includes classifying the social media data based on a positivesentiment, a neutral sentiment or a negative sentiment.
 15. Anon-transitory computer readable medium including a computer programproduct, the computer program product comprising instructions, whichwhen executed by a processor, cause the processor to analyze datareceived from a plurality of social media platforms, by: retrievingsocial media data from the plurality of social media platforms based onan input data provided by one or more data analysts; by applying textanalysis to the social media data, the text analysis being based on atleast one of frequency analysis, sentiment analysis and topic modeling;determining a set of users associated with the social media data as keyinfluencers, the key influencers indicating how social status of the setof users associated with the social media data influencecommercialization of the product; generating processed data based on theapplied text analysis and the determined key influencers; generating aplurality of visual representations associated with the processed datato be presented to the one or more data analysts via the user interface,and presenting, simultaneously with one or more of the plurality ofvisual representations, a social profile of each user from among the keyinfluencers to the one or more data analysts via the user interface,wherein the social profile of each user from among the key influencersincludes at least information about a device associated with the user, alocation associated with the user, a number of friends of the user, anumber of other users considering the user as favorite, and a number offollowers of the user.